agentpit examples

Reference trading agents for agentpit — a paper-trading exchange that mirrors real Polymarket order books. Real prices, real books, no real money.

Two files' worth of agent, at two levels. Start with the script to see what an agent is; move to the skill when you want it running without you.

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1. The script — understand the loop

reference_agent.py is the whole thing in one file:

market question -> model estimates a probability -> compare to the price
                -> trade only when the gap is big enough to be worth it

pip install anthropic requests
export AGENTPIT_API_KEY=...      # your agentpit profile page
export ANTHROPIC_API_KEY=...     # your own model key, your own spend
python reference_agent.py

It runs one cycle and exits, printing what it thought about each market and why it did or did not trade.

2. The skill — let it run without you

SKILL.md plus scripts/ is the same logic packaged for OpenClaw, split the way OpenClaw expects: a deterministic prep step, your reasoning, a deterministic finalize step.

openclaw skills install git:<this-repo>
export AGENTPIT_API_KEY=...
openclaw cron add --every 15m "run the agentpit-reference skill"

Two things change versus the script. The reasoning is done by the model OpenClaw already has configured, so there is no second API key. And a scheduler runs it, so it keeps going while you do something else — as long as the machine stays awake.

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What to expect

Run as-is, both of these lose money. That is the honest baseline, and it is worth understanding before you try to fix it.

A liquid market price already aggregates the opinions of people with money at stake, updated continuously. A general-purpose model, working from public training data with a knowledge cutoff, usually knows less than that price does. Whatever small edge survives, the spread takes.

So beating it needs something the market does not have:

• better data — the model reasons over something the price has not absorbed
• a faster read — you act on news before the book does
• a market nobody priced — thin books where the quote is one person's guess rather than a consensus (though thin books are thin for a reason: the spread is wide and you cannot get size in)

Finding one of those is the exercise. These files are the harness you plug it into, and agentpit is where you find out whether it worked without paying to learn.

Things worth knowing before you start

Do not show the model the market price. Given the price, a model anchors to it and hands back something close — which reads as confirmation and is really an echo. Your backtest looks fine; your P&L does not. The skill enforces this structurally: prep writes the question and the price to different files, and the agent is only ever pointed at the question.

Route by topic, do not average across topics. An ensemble of a politics expert, a crypto expert and a sports expert sounds like wisdom of crowds, but on any single market only one of them knows anything — the other votes are noise outvoting the one informed opinion. Send each market to the agent that covers it. agentpit tags every event with a category, so the routing key is already there.

Aggregate probabilities, not votes. These markets are about how likely, not which way. A 7-to-5 majority is a weak signal that should usually mean "no trade"; a vote turns it into a trade anyway.

A huge edge is a bug report about yourself. The first live run of this bot found a market priced at 0.94 where it estimated 0.15, and happily bought the other side. A 79-point disagreement with a market that has money on it is not an opportunity — it means the resolution criteria say something other than what you assumed, or something happened that you have not seen. MAX_PLAUSIBLE_EDGE skips those. Raise it only once you know why the gap is there.

Mind the gate, not the model. Every market that reaches the model costs a call. Filtering well is worth more than upgrading the model, in both accuracy and spend.

Layout

SKILL.md                    what the agent is told to do
scripts/common.py           agentpit client + the gate + the knobs
scripts/prep.py             step 1: pick markets, write the questions
scripts/finalize.py         step 3: compare to price, place orders
reference_agent.py          standalone, one cycle, your own model key

SKILL.md sits at the repository root because that is where openclaw skills install looks for it — one repository is one skill, so a second example agent means a second repository.

Licence

MIT — fork it, change it, keep it.